Financial Conduct Authority

This paper explores the effect of digital skills at the individual, company and national levels to meet the digital era. UK and European Union based case studies are utilised to fully understand how jurisdictions are preparing stakeholders with confidence and digital tools capability and mindsets. The paper explores how large financial services employers and EdTech are striving to meet the challenges of the accelerating digital era. Finally, it aims to further encourage momentum to close the gap in digital skills by preparing stakeholders for success in the digital age. It concludes that the option of not evolving with technologies and data through new digital skills is no longer feasible for successful careers and industries.

Abstract We present a comprehensive comparative case study for the use of machine learning models for macroeconomics forecasting. We find that machine learning models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggregate information derived from both approaches is broadly in line, Shapley values offer several advantages, such as the discovery of unknown functional forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.

Abstract The Fix for precious metals is a global pricing benchmark that provides pricing and liquidity provision for market participants. We exploit the gradual change in the century old auction process to quantify the efficiencies related to more transparent pricing. Our focus is in the market impact of this change on exchange listed products. We find that reforms to the Fix have reduced quoted and effective bid‐ask spreads and improved overall market depth. The results imply a positive spillover effect stemming from timelier and more accurate pricing information. The conditions under which we observe the benefits from transparency are related to product liquidity and the degree of market segmentation.

SUMMARY Pablo Zbinden?>We consider the drivers and implications of the growth of ‘BigTech’ in finance – i.e. the financial services offerings of technology companies with established presence in the market for digital services. BigTech firms often start with payments. Thereafter, some expand into the provision of credit, insurance and money management products, either directly or in cooperation with financial institution partners. Focusing on credit, we show that BigTech firms lend more in countries with less competitive banking sectors and less stringent bank regulation. Analysing the case of Argentina, we find support for the hypothesis that BigTech lenders, by acquiring a vast amount of non-traditional information, have an advantage in credit assessment relative to a traditional credit bureau. They also serve unbanked borrowers, and may have an advantage in contract enforcement. It is too early to judge the extent of BigTech’s eventual advance into the provision of financial services. However, the early evidence allows us to pose pertinent questions that bear on their impact on financial stability and overall economic welfare.